基于LSSVM-ARMA模型的基坑变形时间序列预测

如何准确预测和控制基坑变形是基坑工程的一个难点,提出了一种基于小波变换、粒子群优化的最小二乘支持向量机(PSO-LSSVM)和自回归移动平均模型(ARMA)的基坑变形时间序列预测方法。首先,利用小波变换将基坑变形时间序列分解和重构为2个子序列——趋势时间序列和随机时间序列,在该基础上,采用PSO-LSSVM模型与ARMA模型分别预测趋势时间序列与随机时间序列未来值,将2个子序列的预测值求和作为最终预测结果。最后,将该方法应用于昆明某基坑工程的深层水平位移预测,不断地利用前期工况的最新实测数据建模,对后期工况未来变形量进行滚动预测,获得了令人满意的结果。

Time series forecast of foundation pit deformation based on LSSVM-ARMA model

It is difficult to forecast and control the deformation of foundation pit engineering. A time series forecast method of foundation pit deformation based on wavelet transform, least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) and autoregressive moving average model (ARMA) is proposed. Firstly, the foundation pit deformation series is decomposed and reconstructed into trend series and random series. Secondly, the trend series future values are forecasted by PSO-LSSVM model; while the random series future values are forecasted by ARMA model. Finally, the sum of trend series and random series future values are used as the final forecast value. This method is used to forecast the deep horizontal displacement of a foundation pit in Kunming. Wherein, the latest measured data of early working stage is used to build forecast model; and the model is used to forecast the later working stage deformation. The result is satisfied.